Today, computerized machines are used to perform tasks in almost all aspects of life, such as handling purchases at store checkout stands, and taking and tracking orders at Internet shopping sites, packaging and sorting merchandise, keeping track of inventory in warehouses, tracking automobile registration data, medical screening for various conditions, and detecting the presence of certain objects or conditions. In some instances, there is a single machine that handles all the transactions or activities for that organization. However, in most cases, there are many machines at different locations handling similar tasks. For example, hospitals may have different campuses with a number of MRI machines in different parts of the campuses. Similarly, grocery store chains may have many stores and warehouses across a large geographical area, each store having a number of checkout registers. Likewise, farmers and orchards may each have their own facilities to automatically sort their produce, like sorting apples into high and low grade. Such sorting machines are often based on the appearance of the product, like in the case where a video camera is used to identify bad fruits based on an automatic classifier.
There is an inefficiency stemming from the fact that the different machines are run and updated separately and independently from one another. While a huge amount of data is collected by each machine, the different machines are unable to “coordinate” with each other or learn from each other. Although the machines often have human operators attending to them to deal with any unusual situations or malfunctions, each of the operators only know what is happening with the subset of machines that he is in charge of, and does not benefit from the data in other machines. This lack of communication and shared newly learned features between machines creates inefficiency and redundancy that result in errors. In one instance, a shopper looking for a specific item may have no quick and easy way of knowing which nearby stores carry the item he is looking for. In this kind of situation, much time is wasted by the shopper finding out the phone numbers and calling each of the nearby stores to do a stock check. In another instance, a medical diagnostic machine that has few patients with fractures and utilizes its original core detection algorithm would remain with same detection capability for a long time, keeping it inferior to a diagnostic machine located at a sports medicine center that would continuously get smarter from being exposed to larger samples of such fractures. In yet another instance involving produce classification machines, an operator would have to adjust each machine individually to make sure it weeds out produce with a certain new condition that would be unappealing to customers. In yet another instance involving object detection machines scanning employees' bags for prohibited items (e.g., alcohol, cigarettes) bags of an employee from a town whose lunches contain items that are unique to that area might get misinterpreted as a bag with a prohibited content, because the machine at corporate headquarters is unaware of bag content types of other towns.
An intelligent system that eliminates the inefficiency and redundancy and increases the accuracy by allowing machines to coordinate, communicate, and learn from each other is desired.